This course is about regression methods. In regression we're working primarily with real valued responses. The main tool for regression is the linear model, in all it's glory ranging from the humble one sample t test to more elaborate methods like splines and wavelets. We also look at competing methods that are sometimes better than linear regression, because the focus is on the problems not the tools.
There will be about 5 problem sets and a final exam. Students are expected to use R to do the problem sets.
The final exam is a take home due on Monday December 12 at 11:30.
Here is the syllabus
The supplementary text is
``Introductory Statistics with R''
by Peter Dalgaard.
Available online from Stanford accounts
here.
That book explains how to use R. If you already know how to use R you don't need to buy it. There are R tutorials below as well.
I expect to send a small number of important emails about problem sets and the homework there. Most other announcements will be made in class. If you email me about the class, be sure to have stat 305 in your subject line. Otherwise, your email won't show when I search for course related emails.Late penalties apply:
We will count days late on each problem set. Each day late is penalized by 10% of the homework value. Homework more than 3 days late will ordinarily get 0. If you're travelling, you can email a pdf file. For sickness, interviews and other events, up to 3 late days total are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)
John Ioannidis
explains why published research findings are false. This is scary stuff. Almost nobody believes that the errors he talks about apply to them.xkcd on correlation versus causation
